Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Chen, Fei; Tian, Wenjing; Zhang, Liyao; Li, Jiazheng; Chen, Ding; Chen, Diyi; Wang, Weiyu; Wu, Feifei; Wang, Bin

doi:10.3390/e24081135

Cited by 10 publications

(5 citation statements)

References 57 publications

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“…Then, the two values are spliced and fed into a convolutional layer to obtain the weights for each location. Finally, a dot product is taken between the resulting weights and the input feature maps, and the spatial-attentionmechanism-based weighted feature maps can be obtained using Equations ( 16)- (18).…”

Section: Attention Mechanismmentioning

confidence: 99%

“…In [17], by mining the fault information contained in the vibration signals, continuous wavelet transform (CWT) was used for the feature extraction, and the vibration signals were converted into RGB images with a time-frequency relationship; then, an improved convolutional neural network (CNN) model was used to complete the image recognition task of transformer fault diagnosis. In [18], a transformer fault diagnosis method based on the combination of time-shifted multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN) was proposed. The method introduces bubble entropy to overcome the shortcomings of traditional entropy models that rely too much on hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

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Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Zhang,

Sun,

et al. 2024

Applied Sciences

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Aiming at the influence of the fundamental frequency and its harmonics in transformer vibration signals on fault signals, which cause a low fault identification rate and degradation of classification model performance, a new strategy is proposed for fault diagnosis using periodic map spectrum feature maps as input features. In this study, the optimal decomposition parameters were first found adaptively using the VMD improved by the positive cosine optimisation algorithm; then, the transformer vibration signal was modally decomposed, and the periodic map spectrum features were plotted according to the differences in the energy distribution of the different modal components at different frequencies. Finally, the GoogLeNet classification model with the improved attention mechanism assigned different weights to the feature maps in both spatial and channel dimensions to improve the classification accuracy and achieve transformer fault diagnosis. The experimental results verified the validity of the adopted feature map and the proposed model, and the accuracy was significantly improved to 99.04% compared with the traditional GoogLeNet, which is valuable for engineering applications.

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Section: Attention Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Zhang,

Sun,

et al. 2024

Applied Sciences

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“…Extension of other entropies to the multiscale has been done. Thus, in [83], the bubble entropy led to time shift multiscale bubble entropy (TSMBE).…”

Section: Abbreviationsmentioning

confidence: 99%

Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application

Grivel,

Berthelot,

Colin

et al. 2024

Entropy

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In various applications, multiscale entropy (MSE) is often used as a feature to characterize the complexity of the signals in order to classify them. It consists of estimating the sample entropies (SEs) of the signal under study and its coarse-grained (CG) versions, where the CG process amounts to (1) filtering the signal with an average filter whose order is the scale and (2) decimating the filter output by a factor equal to the scale. In this paper, we propose to derive a new variant of the MSE. Its novelty stands in the way to get the sequences at different scales by avoiding distortions during the decimation step. To this end, a linear-phase or null-phase low-pass filter whose cutoff frequency is well suited to the scale is used. Interpretations on how the MSE behaves and illustrations with a sum of sinusoids, as well as white and pink noises, are given. Then, an application to detect attentional tunneling is presented. It shows the benefit of the new approach in terms of p value when one aims at differentiating the set of MSEs obtained in the attentional tunneling state from the set of MSEs obtained in the nominal state. It should be noted that CG versions can be replaced not only for the MSE but also for other variants.

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“…In recent years, entropy, as a non-linear dynamic method, has also been applied in the fields of fault diagnosis and underwater acoustic signal recognition [13][14][15]. It is used to describe the complexity of the signal.…”

Section: Introductionmentioning

confidence: 99%

Triple feature extraction method based on multi-scale dispersion entropy and multi-scale permutation entropy in sound-based fault diagnosis

Zhou

Wang

2023

Front. Phys.

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Fault of rolling bearing signal is a common problem encountered in the production of life. Identifying the fault signal helps to locate the fault location and type quickly, react to the fault in time, and reduce the losses caused by the failure in production. In order to accurately identify the fault signal, this paper presents a triple feature extraction and classification method based on multi-scale dispersion entropy (MDE) and multi-scale permutation entropy (MPE), extracts the features of the signal of rolling bearing when it is working, and uses the classification algorithm to determine whether there is a fault in the bearing and the type of fault. Scale 2 of MDE is combined with scale 1 and scale 2 of MPE as the three features required for the experiment. As a comparison of recognition results, multi-scale entropy (MSE)is introduced. Ten scales of the three entropy are calculated, and all combinations of three feature extraction are obtained. K nearest neighbor algorithm is used for three feature recognition. The result shows that the combination recognition rate proposed in this paper reaches 96.2%, which is the best among all combinations.

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Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network

Cited by 10 publications

References 57 publications

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application

Triple feature extraction method based on multi-scale dispersion entropy and multi-scale permutation entropy in sound-based fault diagnosis

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